Learning Parallel Grammar Systems for a Human Activity Language

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Abstract

We have empirically discovered that the space of human actions has a
linguistic structure. This is a sensory-motor space consisting of the
evolution of the joint angles of the human body in movement. The space of
human activity has its own phonemes, morphemes, and sentences. In
kinetology, the phonology of human movement, we define atomic segments
(kinetemes) that are used to compose human activity. In this paper, we
present a morphological representation that explicitly contains the subset
of actuators responsible for the activity, the synchronization rules
modeling coordination among these actuators, and the motion pattern
performed by each participating actuator. We model a human action with a
novel formal grammar system, named Parallel Synchronous Grammar System
(PSGS), adapted from Parallel Communicating Grammar Systems (PCGS). We
propose a heuristic PArallel Learning (PAL) algorithm for the automatic
inference of a PSGS. Our algorithm is used in the learning of human
activity. Instead of a sequence of sentences, the input is a single string
for each actuator in the body. The algorithm infers the components of the
grammar system as a subset of actuators, a CFG grammar for the language of
each component, and synchronization rules. Our framework is evaluated with
synthetic data and real motion data from a large scale motion capture
database containing around 200 different actions corresponding to verbs
associated with voluntary observable movement. On synthetic data, our
algorithm achieves 100% success rate with a noise level up to 7%.